Functional guilds and drivers of diversity in seaweed-associated bacteria

Abstract Comparisons of functional and taxonomic profiles from bacterial communities in different habitats have suggested the existence of functional guilds composed of taxonomically or phylogenetically distinct members. Such guild membership is, however, rarely defined and the factors that drive functional diversity in bacteria remain poorly understood. We used seaweed-associated bacteria as a model to shed light on these important aspects of community ecology. Using a large dataset of over 1300 metagenome-assembled genomes from 13 seaweed species we found substantial overlap in the functionality of bacteria coming from distinct taxa, thus supporting the existence of functional guilds. This functional equivalence between different taxa was particularly pronounced when only functions involved in carbohydrate degradation were considered. We further found that bacterial taxonomy is the dominant driver of functional differences between bacteria and that seaweed species or seaweed type (i.e. brown, red and green) had relatively stronger impacts on genome functionality for carbohydrate-degradation functions when compared to all other cellular functions. This study provides new insight into the factors underpinning the functional diversity of bacteria and contributes to our understanding how community function is generated from individual members.


Introduction
Gene-centric metagenomic analyses of microbial communities from the same habitat often r e v eal high similarities in their functional gene profiles, despite phylogenetic or taxonomic variability.This has been observed, for example, for microbial communities associated with human guts (Qin et al. 2010 ), rivers (F rossar d et al. 2012 ), soil (Mendes et al. 2015 ), seawater (Louca et al. 2016b ), sponges (Fan et al. 2012 ), plants (Louca et al. 2016a ), bioreactors (Wittebolle et al. 2008 ) and seaweeds (Burke et al. 2011, Roth-Schulze et al. 2018 ).Such functional equivalence on the community le v el can r esult fr om the stoc hastic assembl y of individual comm unities fr om "functional guild" members that can be phylogenetically or taxonomically related to each other, or can come from distinct clades or taxa.(Simberloff and Dayan 1991 ).Multiple guild members may also exist in a single comm unity, wher e they may act as "insur ance" a gainst the loss of a given guild function r esulting fr om an or ganism's disa ppear ance (Eisenhauer et al. 2012 ).
To r e v eal guild membership, the functionality of individual organisms needs to be assessed.Genome-centric functional analysis of metagenome-assembled genomes (MAGs) from different micr obial comm unities, including those in anammox bioreactors (Ali et al. 2020 ), subseafloor aquifers (Tully et al. 2018 ), human guts (Forster et al. 2019 ) and sponges (Engelberts et al. 2020 ), have explored the existence of functional guilds.While useful, these studies hav e onl y consider ed one functional tr ait (e.g.nitr ogen metabolism, sulfur cycling, carbon fixation, B-vitamin synthesis, or taurine metabolism) at a time to determined guild membership.Ho w e v er, an or ganism's ecological function needs to be assessed by the combination of all its traits or functions (Morris et al. 2020 ) and this can be ac hie v ed by assessing its total functional gene profile (Chauhan 2019 ).
Seaweeds (marine macroalgae) harbour complex communities of micr oor ganisms on their surface, whic h a ppear to be essential for the hosts' function, health and performance (Egan et al. 2013, Singh and Red d y 2016, Ghaderiardakani et al. 2020 ).Many studies have explored the taxonomic diversity of seaweed-associated bacteria in different environments and under various conditions (Saha et al. 2020b, Marzinelli et al. 2015, Morrissey et al. 2019, Ca pistr ant-Fossa et al. 2021, Lemay et al. 2021, Ling et al. 2022, van der Loos et al. 2023, Zozaya-Valdés et al. 2017 ).Mor eov er, gene-centric meta genomic anal yses of either differ ent individuals of the same seaweed species (Burke et al. 2011 ), closely related species (Roth-Schulze et al. 2018 ) or distantly related species (Roth-Schulze et al. 2016 ) have revealed that individual hosts have lar ge differ ences in the taxonomic composition of their bacterial communities, but similarity in their community functions .T herefore , sea weeds can be used as models to investigate guild membership in epibiotic bacterial communities.
The physiological traits of a seaweed host likely contribute to determining the functional features of its associated bacteria, including the pol ysacc haride-based cell wall constituents that may work as selective carbon sources for epibionts (Percival 1979 ), secondary metabolites capable of influencing colonization (Saha et al. 2020a, Kessler et al. 2018 ) and/or mor phological featur es (Spoerner et al. 2012, Alsufyani et al. 2020, Lemay et al. 2020, Lemay et al. 2021 ).Apart from such host traits, bacterial taxonomy has also been found to determine the functional gene composition of individual bacterial species found in sponges (Robbins et al. 2021 ) and human guts (Forster et al. 2019 ).The actual drivers of the functional variation of the community members on sea weeds , howe v er, still needs investigation.
To address these knowledge gaps, we generated an extensive data set of the taxonomic and functional diversity of bacteria associated with the three major seaweed groups (brown Phaeoph yceae , green Chloroph yta and red Rhodophyta ) and analysed a large collection of MAGs to examine functional guild membership and potential drivers of functional div ersity.Specificall y, if functional guilds are a feature of seaweed-associated microbial communities, we hypothesised that taxonomically distinct community members would have similar predicted functional profiles, and hence could be considered member of the same functional guild.We also hypothesised that if host traits are a driver of functional div ersity, ther e w ould be an association betw een tr aits (suc h as carbohydrate metabolism) and functional variation between community members.
DNA from the surface microbiota of macroalgae, which were all fr eshl y collected fr om the field, wer e pr e viousl y obtained using the direct enzymatic lysis and extraction method (Burke et al. 2009 ) for A. anceps, C. filliformis, D. pulchra, U .australis, U .ohnoi , and U. rigida or from surface swabs followed by standard DNA extractions as described by Marzinelli et al. ( 2015 ) for D. marginatus, E. r adiata, P. cr assa, P. comosa, S. linearifolium , and S. vestitum .Meta genomic sequences fr om swabs of fr eshl y collected Hormosira banksii wer e gener ated using the methods described in Nappi et al. ( 2022 ).Details about sample collection, DNA extraction method, sequence library preparation, sequencing platforms and accession number of the samples used in the study can be found in Table S1 .
Publicl y av ailable genome sequences of cultured bacteria associated with marine macroalgae were obtained from the Genomes Online database (GOLD) (Mukherjee et al. 2019 ) and the NCBI database.

Gener a tion of metagenome-assembled genomes, taxonomic assignment and phylogenetic analysis
Meta genomic r eads ( Table S1 ) fr om m ultiple runs for a single sample were concatenated, then filtered for adapter sequences, and quality trimmed using Trimmomatic 0.38 (Bolger et al. 2014 ) with the setting of phred33 and SLIDINGWINDOW:4:20.Quality filtered r eads wer e subsequentl y err or corr ected and assembled using metaSPAdes 3.13 (Banke vic h et al. 2012 ) with default par ameters.Quality-filter ed r eads wer e ma pped onto the contigs using bowtie 2.3.4.2 (Langmead and Salzberg 2012 ) and cov er a ge information for each contig was generated using samtools 1.9 (Li et al. 2009 ) and the jgi_summarize_bam_contig_depth (Kang et al. 2019 ) script.
The genome taxonomy database toolkit (GTDB-Tk) 0.3.2(Chaumeil et al. 2019 ) was used to assign taxonomies to each MAG/genome based on the classification of genome taxonomy database (GTDB) (Parks et al. 2018 ).FastTree 2.1.10( Price et al. 2010 ) was used to construct phylogenetic trees with the bacterial concatenated marker gene alignments provided by GTDB-Tk.The inter activ e tr ee of life (iTOL) v5 (Letunic and Bork 2019 ) was used to visualise trees using genome origin and their taxonomic classification.

Functional annotation of MAGs
Prokka v1.14.5 (Seemann 2014 ) was used to pr edict pr otein sequences, whic h wer e annotated with the Cluster of Orthologous Groups (COG) (Galperin et al. 2021 ) (2020 release), the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al. 2019 ) Orthologies (KOs) (July 2018 release) and the Carbohydrate Active Enzymes (CAZy) (Lombard et al. 2014 ) (2020 release) databases.The COG and KEGG databases cov er ed most of the known gene functions , whereas the C AZy database is specialised for carbohydrate metabolism.The Diamond v0.9.24 (Buchfink et al. 2014 ) tool was used with an E-value cut-off of 0.001 for similarity search against COG and KEGG.dbCAN 1.0 (Yin et al. 2012 ) was used with HMMER v3.2.1 (Mistry et al. 2013 ) with an E-value < 1e-18 and cov er a ge > 0.35 for annotation with the C AZy database .Annotations were performed with in-house python scripts ( https: // github.com/songweizhi/ BioSAK ).
The R pac ka ge mv abund v4.1.3(Wang et al. 2012 ) was used to fit m ultiv ariate gener alised linear models (GLMs) to test the effects of

Taxonomic and phylogenetic di v ersity of MAGs from the seaweed microbiome
Using publicly available and newly generated metagenomic datasets for seaweed-associated microbiomes ( Table S1 ), 4066 MAGs wer e gener ated for thirteen differ ent marine macr oalgal species.In addition, 659 pr e viousl y gener ated MAGs fr om Ec klonia radiata were added to the analysis (Song et al. 2021 ).MAGs wer e der eplicated within seaweed species and this resulted in 389 MAGs of high quality (95.23 ± 2.69% completeness and 1.14 ± 1.06% contamination) and 913 of medium quality (69.95 ± 13.22% completeness and 2.82 ± 2.76% contamination) (Table 1 ).The aver a ge estimated MAG genome size was 3.84 ± 2.07 Mbp.
Taxonomic and phylogenetic analysis of the 1312 MAGs rev ealed the pr esence of a div erse set of bacterial linea ges (Fig. 1 and Fig. S1 ), which included a total of se v enteen phyla dominated by the Pseudomonadota (pr e viousl y Pr oteobacteria) (644 MAGs), Bacteriodota (323), Planctomycetota (129), Verrucomicrobiota (83) and Actinobacteriota (66) (together covering 94.89% of all MAGs) ( Table S2 ).No archaeal MAG was r ecov er ed.The MAGs were further assigned to 25 classes, 64 orders and 88 families .T hree MAGs (aa_83_Cluster.2,aa_83_metabat_bin.12 and er_BI_ER_110 816_Refined_6) could not be assigned to any known order.The number of MAGs not assigned to any described taxon increased further from the family level to the species level, where more than 98% of MAGs remained unassigned to a species ( Fig. S2 ).
Only 19 genomes sequences for cultured bacteria from marine macr oalgae wer e publicl y av ailable at the time of this study ( Fig. S3 ).Bacteria from the phyla Pseudomonadota and Bacteroidota comprised most of these genomes and their diversity at lo w er taxonomic le v els was limited.The gener a Aquimarina, Maribacter, Polaribacter, Alteromonas, Epibacterium, Phaeobacter and Ruegeria were found in both the MAGs and the isolate genomes.In contrast, the genera Arenibacter , Formosa, Maribacter , Polaribacter , Zobellia, Alkalibacterium, Ferrimonas, Kiloniella, Microbulbifer and Shewanella were only found in the isolate genomes.No correlation was apparent between MAG phylogeny or taxonomy with host seaweed groups or species (Fig. 1 and S1 ).

Functional gene profiles in the seaw eed-associa ted bacteria
Seaweed-associated MAGs were annotated against three databases (COG, KEGG and CAZy) to describe their functional potential.Av er a ge annotations per detected open reading frame (ORF) for COG, KEGG and CAZy were 77.06% ± 9.17; 43.80% ± 9.50 and 2.17% ± 1.35, r espectiv el y, and 4305 functions were annotated against COG, 5943 for KEGG (Level 4) and 383 for the C AZy database .
To observe the functional relatedness between MAGs, nonmetric multidimensional scaling (NMDS) plots were generated based on the predicted gene profiles.Broad clustering of the MAGs based on their taxonomic grouping was observed for COG and KEGG (Le v el 4) annotations (Fig. 2 and S4, r espectiv el y).At the phylum le v el, se v er al clusters ov erla pped forming gr oups wher e MAGs with different taxonomies share the same or very similar functional profiles (see arrows in Fig. 2 ).These ov erla ps included one group consisting of members from the phyla Acidobacteriota, Bacteriodota, Planctomycetota and Verrucomicrobiota, a second between Actinobacteriota and Pseudomonadota and a third dispersed group consisting of MAGs predominately belonging to the phylum Patescibacteria, but also containing members of the Bacteriodota and Verrucomicrobiota.At the class level, the Alphaproteobacteria and Gamma pr oteobacteria formed one group, as did the Bacter oidia, Physicphaer ae , Planctomycetes , Verrucomicrobiae and UBA1135, and the Paceibacteria and Gracilibacteria   clusters ov erla pped.At the order le v el, MAGs ov erla pped to form two main gr oups, one r epr esented by the orders Caulobacterales, Enter obacter ales, Gr anulococcales, Micavibrionales, Pseudomonadales , Rhizobiales , Rhodobacterales , Sphingomonadales , T hiotricales and UBA10353, and the other by Chitinophagales , Fla vobacteriales, Pirellulales and Verrucomicrobioales .T he family level plots follo w ed similar patterns as the or der le v el.
For CAZy-based functional annotations, taxonomy-based clustering of MAGs was not as a ppar ent as the patterns observed for COG and KEGG pr ofiles.Instead, e v en at higher taxonomic le vels the MAGs from different taxonomic groups substantially overlapped (Fig. 3 ).To further investigate the distribution of carbohydrate metabolism functions across MAG phylogeny, the relative abundances of CAZy gene functions (n = 98) that significantly differed between host types (i.e.br own, gr een and r ed seaweeds) wer e gr ouped into six br oad functional categories (AA = Auxiliary Activity, CBM = Carbohydrate-Binding Module, CE = Carbohydr ate Ester ase, GH = Gl ycoside Hydr olase, GT = Gl ycosyl Tr ansferase and PL = Polysaccharide L yase).W e found that CAZy functional categories were evenly distributed across the phylogenetic breadth of the MAGs (Fig. 4 ).This observation supports the notion that carbohydrate metabolic functions are weakly aligned with bacterial taxonomy in seaweed-associated bacteria and that different taxa could perform similar specialized functions in the context of carbohydrate utilization.Ho w ever, some exceptions wer e observ ed, for example, MAGs fr om the phylum P atescibac-teria were enriched for genes encoding glycosyl transferases (GT), but lacked other C AZymes .On the class level, the distribution of carbohydrate metabolic functions mostly follo w ed the patterns of phylum-le v el though MAGs fr om the class Alpha pr oteobacteria had fewer CAZymes from the CBM group compared to other classes.

Predictors of functional di v ersity in seaw eed-associa ted bacteria
Multiv ariate anal yses wer e used to assess and quantify the effect of seaweed group, seaweed species and bacterial taxonomy on the predicted functionality of seaweed-associated bacteria (Table 2 ).Bacterial taxonomy at different levels explained the variation of more than 3500 functions in both COG and KEGG annotations (or ∼ 63%-84% of all functions).Seaweed groups could explain the variation in ∼300 ( ∼6%-∼9%) COG and KEGG (Level 4) functions and seaweed species could explain the variation of ∼500 ( ∼12%) COG and ∼750 ( ∼13%) KEGG (Le v el 4) functions.Bacterial taxonomy could explain ∼50% of the carbohydrate metabolism functions , while sea weed groups and species explained ∼19% and ∼26% of functions, r espectiv el y.This shows that bacterial taxonomy is less of a driver relative to seaweed group or seaweed species when the functions for carbohydrate metabolism (CAZy) ar e consider ed compar ed to when all cellular functions (COG and/or KEGG) are taken into account.

Taxonomic di v ersity and phylogenetic breadth of seaw eed-associa ted bacteria
The diversity of bacteria associated with different seaweed species has been pr e viousl y explor ed mainl y using 16S rRNA gene sequencing and gene-centric metagenomics (Saha et al. 2020b, Marzinelli et al. 2015, Roth-Schulze et al. 2018, Morrissey et al. 2019b, Tourner oc he et al. 2020, Ca pistr ant-Fossa et al. 2021, Lemay et al. 2021, Ling et al. 2022a, van der Loos et al. 2023, Zozaya-Valdés et al. 2017 ) and more recently using genome-centric analyses of the kelps Macrocystis pyrifera (Vollmers et al. 2017 ), Ecklonia radiata (Song et al. 2021 ) and Nereocystis luetkeana (Weigel et al. 2022 ).In this study, the majority ( ∼95%) of the 1312 MAGs fr om differ ent seaweed species fr om all thr ee major gr oups (i.e.br own, gr een, and r ed) could be assigned to one of fiv e main phyla (Pseudomonadota, Bacteriodota, Planctomycetota, Verrucomicrobiota, or Actinobacteriota) consistent with previous studies.No MAGs were recovered for the Archaea, which have previousl y onl y once been r eported for thr ee seaw eed species in lo w r elativ e abundances (Trias et al. 2012 ).This indicates that members of the Archaea are not prominent in seaweed-associated micr obial comm unities, and this contr asts with other marine holobionts, suc h as cor als or sponges (Wegley et al. 2004, Robbins et al. 2021 ).Some of the less dominant phyla observed in this study (i.e.Acidobacteriota, Chloroflexota, Cyanobacteria, and Firmicutes) have also been reported to be either less abundant (Saha et al. 2020b, Lachnit et al. 2011, Mancuso et al. 2016, Lemay et al. 2018, Morrissey et al. 2019, P arr ot et al. 2019, Tourner oc he et al. 2020 ) or r ar el y found on sea weeds (e .g. Bdello vibrionata (Liu et al. 2020 ) and Patescibacteria (Tourneroche et al. 2020 )), although bloom or aquaculture conditions can impact the r elativ e pr oportion of these taxa compared to field/wild samples (Califano et al. 2020 ).Inter estingl y, we also identified MAGs belonging to the novel candidate phylum Eremiobacterota, a group that consists of metabolic diverse individuals, including those rich in novel biosynthetic gene clusters.Ca.Eremiobcterota have been found in association with boreal mosses (Ward et al. 2019 ), Antarctic soils (Ji et al. 2021 ) and more recently seawater (Paoli et al. 2022 ), but to the best of our knowledge, not on seaweeds.Indeed, a significant degree of novel taxonomic diversity was found here as many of the MAGs remained unassigned to any described taxon, highlighting the value of using this a ppr oac h to uncover novel bacterial biodiversity associated with sea weeds .

Functional guilds in seaw eed-associa ted bacteria
Some seaweed-associated bacteria from distinct taxonomic groups had functional gene profiles that closely resembled each other, y et w er e differ ent fr om members of their own taxon, and this pattern was observed at v arious r anks (fr om phylum to famil y).This observ ation supports the notion of functional guilds consisting of taxonomically distinct bacteria in the seaweed microbiome .T he lack of strict linkage between taxonomy and function was particularly striking when considering the carbohydrate metabolism functions, where CAZy functional gr oups wer e found e v enl y distributed among most phyla and classes .T he wide distribution of GTs might be explained by their essential roles in the biosynthesis of cellular oligo-and pol ysacc harides, as well as pr otein gl ycosylation (Lairson et al. 2008 ) and hence is likely unr elated to an y specific ecological function.Ho w e v er, GHs ar e often found in different marine bacteria capable of degrading various seaweed pol ysacc harides like fucoidan (Dong et al. 2017 ), ulvan (Reisky et al. 2019 ) and carrageenan (Ficko-Blean et al. 2017 ) and this might constitute a specific adaptation to a seaweedassociated lifestyle driven by carbohydrate degradation.The only exception here are the Patescibacteria, which are depleted of most C AZy functions , potentially due to their reduced genome size (Tian et al. 2020 ).Inter estingl y, MAGs fr om the class Alpha pr oteobacteria lac ked genes r esponsible for the carbohydr atebinding module (CBM) compared to other classes .T his was also noticed in marine particle-associated bacteria, where the r elativ e abundance of genes encoding CBM functions in alpha pr oteobacterial isolates was lower than the gamma pr oteobacterial ones, and especially in the epipelag ic reg ions (Zhao et al. 2020 ), where most seaweeds are found.

Dri v ers of functionality of seaw eed-associa ted bacteria
While bacterial taxonomy, seaweed group and seaweed species could, to different degrees, explain the distribution of gene functions in seaweed-associated bacteria, bacterial taxonomy was quantitativ el y the most pr ominent driv er of functionality.This is similar to the patterns observed for COG functional categories in bacterial genomes from the GTDB database and a diverse set of en vironments , where 41.1% of functional variation could overall be explained by taxonomy (Ro y alty and Steen 2019 ).The variation explained by taxonomy furthermore also differed between functional categories, with, for example, inorganic ion, amino acid and coenzyme transporters being largely influenced by phylum-level taxonomy.Ho w e v er other functions might be uncoupled from or weakly influenced by taxonomy as has been seen, for example, for antibiotic resistance functions in aquatic environments (Fang et al. 2019 ).Bacterial taxonomy explained here the variation of a lo w er proportion ( < 58%) of all specialized function related to carbohydrate metabolism (CAZy) than compared to all cellular functions ( > 79%).Instead, seaweed group and species explained relatively more of the variation for CAZy functions (19% and 26%, respectiv el y).This partial decoupling of carbohydrate metabolism from taxonomy is consistent with observations made by Martiny et. al. ( 2013 ), who reported that the ability of carbon utilization of hundreds of bacterial isolates was phylogenetically highly dispersed.Similarl y, the taxonomic r ank of famil y was pr e viousl y shown to be a poor predictor of the GH and PL CAZyme gene abundances in human-associated bacterial genomes (Cantarel et al. 2012 ).Together our data and these studies support the conclusion that bacterial taxonomy, especially at higher taxonomic ranks, can onl y weakl y explain the distribution of carbohydrate metabolism in bacteria.
Carbohydr ates ar e important mediators in the host-micr obe interactions (Hooper et al. 2002 ) and their metabolism by microor-ganisms has been found to be influenced by hosts.For example, in the human gut, bacterial carbohydrate metabolism is mediated by the constituents of the diets (plant-based dietary fibres) and thus the host (Cronin et al. 2021 ).In the case of sea weeds , different gr oups (i.e.br own, gr een v ersus r ed) possess differ ent pol ysacc harides (i.e .fucoidan, alginate , ulv an, a gar, carr a geenan, etc) as their cell wall constituents, whose availability and consumption could be considered a strong deterministic host trait influencing the assembly of epibionts (Egan et al. 2013 ).Pr e vious studies using genecentric metagenomics already revealed that the community-wide functional potential for degradation of specific pol ysacc harides was correlated with different types of seaweed hosts (Burke et al. 2011, Roth-Schulze et al. 2016 ), and here w e sho w that this also determines the functionality of specific community members.

Conclusion
In this study, the microbiome of 15 different seaweed species was found to harbour a vast range of novel bacterial taxa and future exploration of additional seaweed species will likel y uncov er e v en more taxonomic and functional diversity.Using a genome-centric a ppr oac h, this study further r e v ealed a high degree of functional similarity of taxonomically distinct bacteria, which supports the notion that functional guilds exist in the seaweed microbiome.Ho w e v er futur e experimental work will be r equir ed to define the exact membership of these guilds both in terms of taxonomy and function.Despite bacterial taxonomy explaining the variation of most gene functions across seaweed-associated bacteria, host seaweed type and seaweed species also make significant contributions to the functional div ersity, particularl y when specialised functions such as carbohydrate utilisation are considered.Our results provide an important framework for further research that will be r equir ed to determine the precise threshold of functional similarity r equir ed for guild membership and for whic h m ultiple organisms can be considered functionally redundant.

Figure 1 .
Figure 1.Phylogenetic tree of 1312 metagenomic assembled genomes (MAGs) and genomes of cultured bacteria associated with marine macroalgae.Blue strips in the inner ring mark genomes from cultured bacteria.Second, third and fourth ring from the inside represent phylum-, class-, and order-le v el taxonomy of MAGs with coloured explained in the figure panels.Outer ring is colour-coded by the major seaweed groups (brown, green and red).

Figure 2 .
Figure 2. NMDS plots of functional gene profiles based on COG annotation of the MA Gs.MA Gs are coloured by their phylum (A), class (B), order (C) or family (D) level taxonomy.Phyla with a minimum of two, classes with a minimum of five, and orders and families with a minimum of 10 MAGs are plotted.Coloured ellipses depicting 95% confidence intervals around clusters of taxonomic groups.Arrows point to groups of MAGs where taxonomic clustering ov erla ps based on gene functions.

Figure 3 .
Figure 3. NMDS plots of functional gene profiles based on CAZy annotation of the MA Gs.MA Gs are coloured by their phylum (A), class (B), order (C) or family (D) level taxonomy.Phyla with a minimum of two, classes with a minimum of five, and orders and families with a minimum of 10 MAGs are plotted.Coloured ellipses depicting 95% confidence intervals around clusters of taxonomic groups.

Figure 4 .
Figure 4. Phylogenetic distribution of CAZy functional groups across the MAGs.Inner segments show phylum-level taxonomy of MAGs, while inner ring shows class-le v el taxonomy.Colour coding is explained in panels.Second inner ring to outer ring r epr esent the r elativ e abundances of six CAZy functional gr oups.Relativ e abundances ar e colour ed coded by heat ma p show at the bottom left.Labelling of the six rings on the left stands for functional CAZy groups listed at the top right.Only significant functions acquired from multivariate analysis using seaweed species as a fixed factor (n = 98) are shown.Functional diversity in seaweed-associated bacteria is characterised by taxonomy, carbohydrate metabolism and functional guilds.
MAG taxonomy (i.e .phylum, class , order and family) or host type (i.e .sea weed group (i.e .br own, gr een, and r ed) or seaweed species) as fixed factors on the functional gene profiles of the MAGs .T he tests were performed using the function "manyglm" for multivariate r aw, non-tr ansformed gene function count data, including the total count of annotated genes per genome as a model offset and a negative-binomial distribution, with adjusted p-values to account for multiple-testing and using 999 bootstraps.Residual plots, Q-Q plots and mean-variance plots were checked to ensure good model fits .Individual COGs , KEGGs (Le v el 4) or CAZy functions affected by each of the fixed factors was determined using a critical value of alpha = 0.05.The significant effect of each model was determined by comparing fitted models with null models using likelihood ratio tests (LRTs).

Table 2 .
Multiv ariate anal ysis of factors influencing the seaweed-associated MAG functional pr ofiles.Pr edictors wer e fitted as fixed factors to m ultiv ariate gener alised linear models (GLMs) to explain functional gene profiles of MAGs.Likelihood ratio tests (LRTs) results and P -values are reported as well as the number of functions (and proportion of all functions in br ac kets), whose v ariation can be explained by the pr edictor.Anal ysis was done on differ ent functional annotation systems.